The leading theories of sentence planning - Hierarchical Incrementality and Linear Incrementality - differ in their assumptions about the coordination of processes that map preverbal information onto language. Previous studies showed that, in native (L1) speakers, this coordination can vary with the ease of executing the message-level and sentence-level processes necessary to plan and produce an utterance. We report the first series of experiments to systematically examine how linguistic experience influences sentence planning in native (L1) speakers (i.e., speakers with life-long experience using the target language) and non-native (L2) speakers (i.e., speakers with less experience using the target language). In all experiments, speakers spontaneously generated one-sentence descriptions of simple events in Dutch (L1) and English (L2). Analyses of eye-movements across early and late time windows (pre- and post-400 ms) compared the extent of early message-level encoding and the onset of linguistic encoding. In Experiment 1, speakers were more likely to engage in extensive message-level encoding and to delay sentence-level encoding when using their L2. Experiments 2-4 selectively facilitated encoding of the preverbal message, encoding of the agent character (i.e., the first content word in active sentences), and encoding of the sentence verb (i.e., the second content word in active sentences) respectively. Experiment 2 showed that there is no delay in the onset of L2 linguistic encoding when speakers are familiar with the events. Experiments 3 and 4 showed that the delay in the onset of L2 linguistic encoding is not due to speakers delaying encoding of the agent, but due to a preference to encode information needed to select a suitable verb early in the formulation process. Overall, speakers prefer to temporally separate message-level from sentence-level encoding and to prioritize encoding of relational information when planning L2 sentences, consistent with Hierarchical Incrementality.

Our environments are saturated with learnable information. What determines which of this information is prioritized for limited attentional resources? Although previous studies suggest that learners prefer medium-complexity information, here we argue that what counts as medium should change as someone learns an input’s structure. Specifically, we examined the hypothesis that attention is directed toward more complicated structures as learners gain more experience with the environment. College students watched four simultaneous streams of information that varied in complexity. RTs to intermittent search trials (Experiment 1, N = 75) and eye tracking (Experiment 2, N = 45) indexed where participants attended during the experiment. Using two participant- and trial-specific measures of complexity, we demonstrated that participants attended to increasingly complex streams over time. Individual differences in structure learning also predicted attention allocation, with better learners attending to complex structures earlier in learning, suggesting that the ability to prioritize different information over time is related to learning success.

Decades of work has shown that learners rapidly extract structure from their environment, later leveraging their knowledge of what is more versus less consistent with prior experience to guide behavior. However, open questions remain about exactly what is remembered after exposure to structure. Memory for specific associations-transitions that unfold over time-is considered a prime candidate for guiding behavior. However, other factors could influence behavior, such as memory for general features like reliable groupings or within-group positions. We also do not yet know whether memory depends upon the amount of experience with the input structure, leaving us with an incomplete understanding of how statistical learning supports behavior. In 4 experiments, we tracked the emergence of memory for itemitem transitions, order-independent groups, and positions by having 400 adults watch a stream of shape triplets followed by a recognition memory test. We manipulated how closely test sequences corresponded to the input along each dimension of interest, allowing us to isolate the contribution of each factor. Both item-item transitions and order-independent group information influenced behavior, highlighting statistical learning as a mechanism through which we form both specific and generalized representations. Moreover, these factors drove behavior after different amounts of experience: With limited exposure, only group information impacted old-new judgments specific transitions gained importance later. Our findings suggest statistical learning proceeds by first forming a general representation of structure, with memory being later refined to include specifics after more experience.

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